Top Hybrid Machine Learning Engineer Jobs

Develop advanced machine learning and statistical models to support credit risk and operational strategies, collaborating with multiple teams to enhance business profitability.

The Principal Data Scientist will design and develop machine learning systems, manage data pipelines, and collaborate with teams to improve products and services. This role requires significant experience in ML model deployment and communication with stakeholders.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

Contribute to machine learning model design, training, and evaluation for fraud detection. Communicate findings, develop metrics, and support production ML workflows while ensuring compliance and model governance.

Develop and scale machine learning systems for physiological signal processing. Design and train models, ensure compliance, and mentor team members while collaborating with cross-functional teams.

Conduct ML and deep learning research to build, test, and deploy predictive models for trading. Collaborate with traders, researchers, and engineers to engineer features, preprocess data, prototype algorithms, and improve production research tooling to enhance trading performance.

The role involves deploying ML models for ads, collaborating with teams to enhance solutions, and mentoring junior scientists, requiring advanced technical and communication skills.

Design and conduct advanced data analyses and develop machine learning models for mental health research, working with large datasets and multimodal methods.

Work on prototyping and implementing audio ML algorithms for real-time applications. Present findings and integrate solutions into existing platforms. Collaborate with experts in ML and DSP.